Cold chain logistics is one of the most interesting aspects of food logistics. For scientists, it offers numerous complex and interdisciplinary research questions, while for providers, it provides various possibilities to reduce costs and improve food quality. Furthermore, efficiently managed cold chains offer a great potential to provide end customers with fresh and safe products and reduce food waste and losses.
Aung and Chang (2014) provide an excellent overview of main challenges present in cold chain logistics. Temperature and storage durations are seen as the most important factors impacting product shelf lives. Additionally, storage temperatures have to be kept constant as fluctuations, e.g., as a result of frequently closing or opening storage doors, often negatively impact quality. Food products themselves differ substantially with individual product classes (e.g., meat, fruits, vegetables, milk) requiring different temperature ranges to prevent damages and maintain quality and food safety. A common strategy is to classify items into frozen, chilled and ambient products, however, even within these categories, optimal storage temperatures may differ substantially among single products. Consequently, providers need to make decisions on how to group products into different temperatures zones, whereas the best option is highly dependent on the product mix and shipment volumes as well as on the available transport equipment (e.g., multi-temperature zone vehicles, vans, refrigerated trucks) and regional settings.
Closely considering such factors in the routing and scheduling of food shipments enables one to adjust operations and improve stock rotation and delivery operations. If real-time information on storage conditions and food quality-related factors are known (see Sciortino et al., 2016, for more details on data collection), vehicle routes can be adjusted dynamically to prioritize critical shipments. Simulation optimization procedures can be used to predict such food quality developments in different supply chain settings and provide decision-makers the option to investigate various strategies and countermeasures. This potentially can reduce food waste, losses and costs as well as facilitates the development of agile and demand-driven food supply chains.
– Aung, M. M., & Chang, Y. S. (2014). Temperature management for the quality assurance of a perishable food supply chain. Food Control, 40, 198-207. https://doi.org/10.1016/j.foodcont.2013.11.016
– Sciortino, R., Micale, R., Enea, M., & La Scalia, G. (2016). A webGIS-based system for real time shelf life prediction. Computers and Electronics in Agriculture, 127, 451-459. https://doi.org/10.1016/j.compag.2016.07.004
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